Byelab: An agricultural mobile robot prototype for proximal sensing and precision farming (original) (raw)
Related papers
2015
A vision system able to give a punctual estimation of the canopy vigour (volume, leaves’ chlorophyll content) of an orchard is a key-system for implementing Precision Agriculture. Indeed, such a system, composed by Lidar and NDVI sensors, can give all the information necessary for performing some important field-operations (e.g., pruning, spraying) and, above all, for setting-up automatically and in realtime the relative machines. The first issues when implementing a vision system concern: which type and how many sensors using, how making this system move within an orchard. As proved in some preliminary lab trials, the use of two Lidar sensors, vertically-aligned to give a sort of lateral-linear-stereoscopic vision, manages to avoid the presence of the large “projected shadows” (or “blind spots”) originating when using a single sensor to scan a target. Then, this article presents a compact “mobile lab”, based on an electric tracked bins-carrier, able to move off-road within the orchards and equipped with an ad-hoc developed adjustable tubular frame, designed to carry two Lidar sensors in the individuated configuration, together with other six (NDVI) sensors. This frame allows placing the sensors at different heights to ensure the complete scan of the canopy (even with high fruit trees).
A Mobile Laboratory for Orchard Health Status Monitoring in Precision Farming
Chemical Engineering Transactions, 2017
Nowadays, Precision Farming (PF) has been fully recognized for its potential capability to increase field yields, reduce costs and minimize the environmental impact of agricultural activities. The first stage of the PF management strategy is the collection of field and environmental data useful to obtain information about the crop health status in a field or among different fields and to operate suitably in each of these partitions (e.g., distribution of fertilizers, pesticide treatments, differential harvesting), according to a site-specific approach, depending on their actual needs. A mobile vehicle (Bionic eYe Laboratory-ByeLab) has been developed to monitor and sense the health status of orchards and vineyards. The ByeLab is a tracked-bins carrier equipped with different sensors: two LiDARs to evaluate the shape and the volume of the canopy, and six optical sensors to evaluate its chlorophyll content, thus to monitor the health state of the crops. Moreover, a RTK GNSS is used to geo-reference the acquired data and an IMU to records the orientation of the ByeLab during the surveys. In order to reproduce the three-dimensional maps of the shape and the health of the canopy, data-processing algorithms have been developed and customized for the application. The combined use of heterogeneous sensors and a deep analysis of the results permit to define an efficient methodology for the crop monitoring. A Matlab® routine has been implemented for the canopy volume reconstruction and the health status mapping. Moreover, a preliminary diagnostic algorithm exploiting both the LiDAR and the optical sensors information to detect early situations of plants stress has been conceived and implemented. The aim of this paper is to disclose the data-processing algorithms exploited to obtain a precise canopy thickness reconstruction and an accurate vegetative-state mapping. The methodology used to validate the canopy thickness measurements and the vegetative state mapping is also presented. In particular, to validate the thickness results obtained with the mobile ByeLab, they have been compared with the acquisitions made with a fixed terrestrial Laser Scanner showing a good correlation. A representation of the parcels monitored during the in-field surveys and of an entire orchard field is finally provided.
A Tracked Mobile Robotic Lab for Monitoring the Plants Volume and Health
Precision agriculture has been increasingly recognized for its potential ability to improve agricultural productivity, reduce production cost, and minimize damage to the environment. In this work, the current stage of our research in developing a mobile platform equipped with different sensors for orchard monitoring and sensing is presented. In particular, the mobile platform is conceived to monitor and assess both the geometric and volumetric conditions as well as the health state of the canopy. To do so, different sensors have been integrated and effective data-processing algorithms implemented for a reliable crop monitoring. Experimental tests have been performed allowing to obtain both a precise volume reconstruction of several plants and an NDVI mapping suitable for vegetation state evaluations.
Design and development of the architecture of an agricultural mobile robot
Engenharia …, 2011
Parameters such as tolerance, scale and agility utilized in data sampling for using in Precision Agriculture required an expressive number of researches and development of techniques and instruments for automation. It is highlighted the employment of methodologies in remote sensing used in coupled to a Geographic Information System (GIS), adapted or developed for agricultural use. Aiming this, the application of Agricultural Mobile Robots is a strong tendency, mainly in the European Union, the USA and Japan. In Brazil, researches are necessary for the development of robotics platforms, serving as a basis for semi-autonomous and autonomous navigation systems. The aim of this work is to describe the project of an experimental platform for data acquisition in field for the study of the spatial variability and development of agricultural robotics technologies to operate in agricultural environments. The proposal is based on a systematization of scientific work to choose the design param...
A Review of Robots, Perception, and Tasks in Precision Agriculture
Applied Mechanics
This review reports the recent state of the art in the field of mobile robots applied to precision agriculture. After a brief introduction to precision agriculture, the review focuses on two main topics. First, it provides a broad overview of the most widely used technologies in agriculture related to crop, field, and soil monitoring. Second, the main robotic solutions, with a focus on land-based robots, and their salient features are described. Finally, a short case study about a robot developed by the authors is introduced. This work aims to collect and highlight the most significant trends in research on robotics applied to agriculture. This review shows that the most studied perception solutions are those based on vision and cloud point detection and, following the same trend, most robotic solutions are small robots dedicated exclusively to monitoring tasks. However, the robotisation of other agricultural tasks is growing.
Development of an agricultural mobile robot for use in precision agriculture
CIGR …, 2009
The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the International Commission of Agricultural and Biosystems Engineering (CIGR), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by CIGR editorial committees; therefore, they are not to be presented as refereed publications.
Towards a Reliable Monitoring Robot for Mountain Vineyards
2015 IEEE International Conference on Autonomous Robot Systems and Competitions, 2015
Crop monitoring and harvesting by ground robots on mountain vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the GPS system. In this paper is presented a cost effective robot that can be used on these mountain vineyards for crop monitoring tasks. Also it is explored a natural vineyard feature as the input of a standard 2D simultaneous localization and mapping approach (SLAM) for feature-based map extraction. In order to be possible to evaluate these natural features for mapping and localization purposes, a virtual scenario under ROS/Gazebo has been built and described. A low cost artificial landmark and an hybrid SLAM is proposed to increase the localization accuracy, robustness and redundancy on these mountain vineyards. The obtained results, on the simulation framework, validates the use of a localization system based on natural mountain vineyard features.
Chemical engineering transactions, 2017
Flavescence doree and Esca are two of the most important diseases that can affect grapevine. These diseases, if not properly treated in time, are the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Flavescence doree and Esca is carried out manually through visual surveys usually done by agronomists. These activities require enormous amount of time. Up to now, a solution for a fast and early disease detection of these bacterial and fungal attack was not yet developed. Aim of this research was to test if the use of sensors typically employed in precision agriculture and robotics, mounted on different vehicles, can be a useful tool for crop monitoring purposes, principally for the recognition of disease symptoms. Therefore, two prototypes of a mobile laboratory, the ByeLab (Bionic eye laboratory) and the ATV- LAB, equipped with sensors, we...
Electronics, 2020
To help address the global growing demand for food and fiber, selective breeding programs aim to cultivate crops with higher yields and more resistance to stress. Measuring phenotypic traits needed for breeding programs is usually done manually and is labor-intensive, subjective, and lacks adequate temporal resolution. This paper presents a Multipurpose Autonomous Robot of Intelligent Agriculture (MARIA), an open source differential drive robot that is able to navigate autonomously indoors and outdoors while conducting plant morphological trait phenotyping and soil sensing. For the design of the rover, a drive system was developed using the Robot Operating System (ROS), which allows for autonomous navigation using Global Navigation Satellite Systems (GNSS). For phenotyping, the robot was fitted with an actuated LiDAR unit and a depth camera that can estimate morphological traits of plants such as volume and height. A three degree-of-freedom manipulator mounted on the mobile platform...